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\title{Design Space Exploration for High-Level Synthesis based on Multi-objective Island Model Genetic Algorithm}

\author{Christian Pilato and Fabrizio Ferrandi and Daniele Loiacono and Donatella Sciuto and Pierluca Lanzi}

\institute{Politecnico di Milano \\
Dipartimento di Elettronica ed Informazione \\
Via Ponzio 34/G, Milano, Italy \\
\small{\{pilato,ferrandi,loiacono,sciuto,lanzi\}@elet.polimi.it} }
%\institute{Princeton University, Princeton NJ 08544, USA
%\and
%Universit\'{e} de Paris-Sud,
%Laboratoire d'Analyse Num\'{e}rique, B\^{a}timent 425,\\
%F-91405 Orsay Cedex, France}

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\section{Introduction}

\framebox{Introduzione a HLS}

\framebox{Introduzione a genetici}

\framebox{Introduzione a island model}

Island models are a popular and efficient way to implement a genetic algorithm on both serial and parallel machines. 
In a parallel implementation of an Island Model each machine executes a genetic algorithm and maintains its own subpopulation
for search. The machines work in consort by periodically exchanging a portion of their populations in a process called
\textit{migration}. One reason for the improved search quality is that the various islands maintain some degree of indipendence
and thus explore different regions of the search space while at the same time sharing information by means of migration.

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\begin{thebibliography}{1}
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Whitley, D., Rana, S., Heckendorn, R.B.,:
The Island Model Genetic Algorithm: On Separability, Population Size and Convergence.
Editor (1998) 1--17
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%%
%\bibitem {clar:eke:2}
%Clarke, F., Ekeland, I.:
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%%
%\bibitem {mich:tar}
%Michalek, R., Tarantello, G.:
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%
%\bibitem {tar}
%%Tarantello, G.:
%Subharmonic solutions for Hamiltonian
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%
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%Rabinowitz, P.:
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